Sometimes, words fail me in describing the absolute disregard of the placement of NOAA official climate monitoring sites. For example, this one in Clarinda, Iowa submitted by surfacestations volunteer Eric Gamberg:

The MMTS temperature sensor is the short pole next to the half pickup truck.

For those of you that don’t know, this station is located at the wastewater treatment plant there. I’ve written many times about the placement of stations at WWTP’s being a bad idea due to the localized heat bubble that is created due to all the effluent coming though. The effect is especially noticeable in winter. Often you’ll see steam/water vapor in the air around these sites in winter, and more than one COOP observer has told our volunteers that snow sometimes does not stick to the ground at WWTP’s.

The larger pole appears to be a gas burnoff torch for excess methane. I can’t say how often it is activated (note the automatic ignitor circuit on the pole) but I can tell you that putting an official NOAA climate thermometer within a few feet of such a device is one of the worst examples of thoughtless station placement on the part of NOAA I’ve ever seen. Here is an example of a methane burn-off device at another WWTP.

We’ll probably never know what the true temperature is in Clarinda because untangling a measurements mess like this is next to impossible. How many days was Tmin and/or Tmax affected at this location by gas burnoff and to what magnitude? We shouldn’t have to ask these questions.

According to the NCDC MMS database for this station, the MMTS was installed on October 1, 1985. Who knows what the data would have looked like if somebody had thought through the placement. Whether or not the temperature sensor has been significantly affected or not by this placement is not the issue, violation of basic common sense siting guideline that bring the data into question is. Anything worth measuring using our public tax dollars is worth measuring correctly.

Dr. Hansen and Mr. Karl – welcome, feast your eyes on the source of your data. You might want to think about changing this description on the NCDC website for USHCN:

The United States Historical Climatology Network (USHCN) is a high quality, moderate-sized data set of daily and monthly records of basic meteorological variables from over 1000 observing stations across the 48 contiguous United States.

I suggest to NCDC that “high quality” doesn’t really apply in the description anymore.

I really could use some help, especially in Texas, Oklahoma, Alabama, Mississippi, and Arkansas to get the USHCN nationwide climate network survey completed. If you have a digital camera and can follow some simple instructions, why not visit www.surfacestations.org and sign up as a volunteer surveyor. If you can’t help that way, donations to help fund trips such as these that I’ve been doing are greatly appreciated.

UPDATE: Some commenters have suggested that the blink comparator above is wrong due to the fact that the scale on the left changes in offset. I realize that may create some confusion. A couple of clarifications are needed to address that.

First, these graphs are generated by the GISTEMP database, not me. I simply copied both from the GISTEMP website into my animation program. This includes the scale offset which is part of the difference in the original GISTEMP generated images. You can do the same thing also by visiting here: http://data.giss.nasa.gov/gistemp/station_data/ and putting Clarinda in the search box. Use the pulldown menu to select either data set you want. The above is the “combined sources” and also “after homogeneity adjustment”.

Second what is important to note here is that the slope of the trend changes as a result of the adjustment applied by GISS. It becomes more positive in the “homogenized” data set.

Third, in the “homogenized” data set, the past has been cooled, the present also made warmer, making the slope more positive over the timeline. Here is the Clarinda GISTEMP Homogenized data plot overlaid on the “raw” data plot. Again these are the original unmodified GISTEMP generated graphs using a simple cut and paste with transparent background technique:

Click graph for full sized image

Note how the hinge point appears in th 1980’s where the data appears to match. Note also how the divergence between the two data sets increases either direction from this hinge point.

The MMTS temperature sensor is the short pole next to the half pickup truck.

For those of you that don’t know, this station is located at the wastewater treatment plant there. I’ve written many times about the placement of stations at WWTP’s being a bad idea due to the localized heat bubble that is created due to all the effluent coming though. The effect is especially noticeable in winter. Often you’ll see steam/water vapor in the air around these sites in winter, and more than one COOP observer has told our volunteers that snow sometimes does not stick to the ground at WWTP’s.

The larger pole appears to be a gas burnoff torch for excess methane. I can’t say how often it is activated (note the automatic ignitor circuit on the pole) but I can tell you that putting an official NOAA climate thermometer within a few feet of such a device is one of the worst examples of thoughtless station placement on the part of NOAA I’ve ever seen. Here is an example of a methane burn-off device at another WWTP.

We’ll probably never know what the true temperature is in Clarinda because untangling a measurements mess like this is next to impossible. How many days was Tmin and/or Tmax affected at this location by gas burnoff and to what magnitude? We shouldn’t have to ask these questions.

According to the NCDC MMS database for this station, the MMTS was installed on October 1, 1985. Who knows what the data would have looked like if somebody had thought through the placement. Whether or not the temperature sensor has been significantly affected or not by this placement is not the issue, violation of basic common sense siting guideline that bring the data into question is. Anything worth measuring using our public tax dollars is worth measuring correctly.

Dr. Hansen and Mr. Karl – welcome, feast your eyes on the source of your data. You might want to think about changing this description on the NCDC website for USHCN:

The United States Historical Climatology Network (USHCN) is a high quality, moderate-sized data set of daily and monthly records of basic meteorological variables from over 1000 observing stations across the 48 contiguous United States.

I suggest to NCDC that “high quality” doesn’t really apply in the description anymore.

I really could use some help, especially in Texas, Oklahoma, Alabama, Mississippi, and Arkansas to get the USHCN nationwide climate network survey completed. If you have a digital camera and can follow some simple instructions, why not visit www.surfacestations.org and sign up as a volunteer surveyor. If you can’t help that way, donations to help fund trips such as these that I’ve been doing are greatly appreciated.

UPDATE: Some commenters have suggested that the blink comparator above is wrong due to the fact that the scale on the left changes in offset. I realize that may create some confusion. A couple of clarifications are needed to address that.

First, these graphs are generated by the GISTEMP database, not me. I simply copied both from the GISTEMP website into my animation program. This includes the scale offset which is part of the difference in the original GISTEMP generated images. You can do the same thing also by visiting here: http://data.giss.nasa.gov/gistemp/station_data/ and putting Clarinda in the search box. Use the pulldown menu to select either data set you want. The above is the “combined sources” and also “after homogeneity adjustment”.

Second what is important to note here is that the slope of the trend changes as a result of the adjustment applied by GISS. It becomes more positive in the “homogenized” data set.

Third, in the “homogenized” data set, the past has been cooled, the present also made warmer, making the slope more positive over the timeline. Here is the Clarinda GISTEMP Homogenized data plot overlaid on the “raw” data plot. Again these are the original unmodified GISTEMP generated graphs using a simple cut and paste with transparent background technique:

Click graph for full sized image

Note how the hinge point appears around 1980 where the data appears to match. Note also how the divergence between the two data sets increases either direction from this hinge point.

Whether dismissing global warming as a hoax, questioning humanity’s role in it, exaggerating the unknowns, playing down the urgency of action, or playing up the costs, President Donald Trump and his team have served up every flavor of climate denial.

Although the arguments varied—as if they were different shades or stages of denial—they all served the same purpose: to create an exaggerated sense of dispute in order to bolster a case against decisive climate action. The latest gambit is to avoid the subject entirely.

In his announcement last week that he would pull the U.S. out of the Paris climate agreement, Trump didn’t bother addressing… [blah, blah, blah]…

[…]

In Trump’s retelling, the negotiators of the Paris deal were not grappling with a planetary crisis… [they weren’t]…

[…]

To help understand the arguments, we have developed a guide to what the science says about the five types of climate denial we’ve heard from Trump, his team, and their supporters, and how each served as a stepping stone on the path of a retreat from Paris.

‘It’s Not Real’

The deepest shade of denial—outright rejection of global warming—is embodied by Trump’s infamous 2012 tweet that called global warming a Chinese plot to make U.S. manufacturing non-competitive.

[…]

To the hard-core unbelievers, climate scientists are conspirators in it for the grant money. They are not to be trusted, deputy national security adviser K.T. McFarland suggested last month by giving Trump a print-out of a purported 1970s TIME magazine cover predicting a coming ice age. (The cover is an internet fake that has been circulating for years. It was cited last year by White House strategist Stephen Bannon in a radio interview he did while running the conservative media outlet Breitbart.)

TIME, like most of the mainstream-ish media, has acted like a climate weathervane over the years…

As you may have noted, the rate at which I have posted new stories in the last couple of days has tapered off, and likely will remain at a lowered rate in the immediate future.

The reason is twofold:

1) Like many people in this country, I’m getting hit economically. My weather business needs my attention more than ever to keep it running and my family supported. The volume of email alone I get daily asking for advice, files, help with research etc. since starting the blog is overwhelming as it is. Often I find WUWT creeping into my business hours, and this can’t continue under the current economic situation. Thus I’m limiting my interaction to late nights and weekends.

2) I’ve realized that WUWT, while important in it’s own right, being now the number one climate related blog (in terms of traffic) on the web has also become a hungry monster for my time. So, what time I have had in the last few days has been focused on the surfacestations project. I’m making a push to get a majority of stations (my goal is at least 75%) surveyed so that a dataset with a better spatial distribution of stations exists. Right now we still have some big holes in it, particularly in Texas, Oklahoma, Arkansas, Kentucky, Alabama, and Mississippi.

Getting stations surveyed and this project database more complete is a much better use of my available spare time than moderating some of the daily philosophical arguments and news items of this blog. I hope my team of crack moderators will be able to fill in the gap and continue to offer postings of relevant stories while I focus on this.

John Goetz and Denise Norris have made some really valuable story contributions to this readership, as has Evan Jones. I hope they’ll be able to continue.

Some folks have commented that becuase I’ve posted my “How not to measure temperature…” series, that I’m only focused on finding the badly sited stations. While they are a dime a dozen and often visually entertaining, actually what we want to find are the BEST stations. Those are the CRN1 and 2 rated stations. Having a large and well distributed sample size of the best stations will help definitively answer the question about how much bias may exist as a result of the contribution of badly sited stations. Since the majorty of sttaions surveyed so far seem to be CRN 3,4,5 with CRN1,2 making up only 12% of the total surveyed stations thus far, it is important to increase the sample size.

So while WUWT will continue to have news and science items of interest, my focus will be getting surveys done, so we’ll see more items on the surfacestations project.

On a related note, I wish to sincerely thank all those that have generously donated to the surfacestations.org project to offset travel expenses. Thanks to that, recently I was able to complete all of the state of Nevada USHCN stations.

Dan Gainor compiled a great timeline of media alarmism (both warming and cooling) in his Fire and Ice essay.

As you may have noted, the rate at which I have posted new stories in the last couple of days has tapered off, and likely will remain at a lowered rate in the immediate future.

The reason is twofold:

1) Like many people in this country, I’m getting hit economically. My weather business needs my attention more than ever to keep it running and my family supported.

2) I’ve realized that WUWT, while important in it’s own right, being now the number one climate related blog (in terms of traffic) on the web has also become a hungry monster for my time. So, what time I have had in the last few days has been focused on the surfacestations project. I’m making a push to get a majority of stations (my goal is at least 75%) surveyed so that a dataset with a better spatial distribution of stations exists. Right now we still have some big holes in it, particularly in Texas, Oklahoma, Arkansas, Kentucky, Alabama, and Mississippi.

Getting stations surveyed and this project database more complete is a much better use of my available spare time than moderating some of the daily philosophical arguments and news items of this blog. I hope my team of crack moderators will be able to fill in the gap and continue to offer postings of relevant stories while I focus on this.

John Goetz and Denise Norris have made some really valuable story contributions to this readership, as has Evan Jones. I hope they’ll be able to continue.

Some folks have commented that becuase I’ve posted my “How not to measure temperature…” series, that I’m only focused on finding the badly sited stations. While they are a dime a dozen and often visually entertaining, actually what we want to find are the BEST stations. Those are the CRN1 and 2 rated stations. Having a large and well distributed sample size of the best stations will help definitively answer the question about how much bias may exist as a result of the contribution of badly sited stations. Since the majorty of sttaions surveyed so far seem to be CRN 3,4,5 with CRN1,2 making up only 12% of the total surveyed stations thus far, it is important to increase the sample size.

So while WUWT will continue to have news and science items of interest, my focus will be getting surveys done, so we’ll see more items on the surfacestations project.

On a related note, I wish to sincerely thank all those that have generously donated to the surfacestations.org project to offset travel expenses. Thanks to that, recently I was able to complete all of the state of Nevada USHCN stations.

The Other Shades of Climate Denial

Debunking Shade #2 “It’s Not Our Fault”

It’s not all our fault. The mythical 97% consensus only asserts that it’s at least half our fault. We certainly could be responsible for some of the warming that has occurred over the past 150 years. The point is that the warming observed in the instrumental temperature record doesn’t significantly deviate from the pre-existing Holocene pattern of climate change…

The essay below has been part of a back and forth email exchange for about a week. Bill has done some yeoman’s work here at coaxing some new information from existing data. Both HadCRUT and GISS data was used for the comparisons to a doubling of CO2, and what I find most interesting is that both Hadley and GISS data come out higher in for a doubling of CO2 than NCDC data, implying that the adjustments to data used in GISS and HadCRUT add something that really isn’t there.

The logarithmic plots of CO2 doubling help demonstrate why CO2 won’t cause a runaway greenhouse effect due to diminished IR returns as CO2 PPM’s increase. This is something many people don’t get to see visualized.

One of the other interesting items is the essay is about the El Nino event in 1878. Bill writes:

The 1877-78 El Nino was the biggest event on record. The anomaly peaked at +3.4C in Nov, 1877 and by Feb, 1878, global temperatures had spiked to +0.364C or nearly 0.7C above the background temperature trend of the time.

Clearly the oceans ruled the climate, and it appears they still do.

Let’s all give this a good examination, point out weaknesses, and give encouragement for Bill’s work. This is a must read. – Anthony

Adjusting Temperatures for the ENSO and the AMO

A guest post by: Bill Illis

People have noted for a long time that the effect of the El Nino Southern Oscillation (ENSO) should be accounted for and adjusted for in analyzing temperature trends. The same point has been raised for the Atlantic Multidecadal Oscillation (AMO). Until now, there has not been a robust method of doing so.

This post will outline a simple least squares regression solution to adjusting monthly temperatures for the impact of the ENSO and the AMO. There is no smoothing of the data, no plugging of the data; this is a simple mathematical calculation.

Some basic points before we continue.

– The ENSO and the AMO both affect temperatures and, hence, any reconstruction needs to use both ocean temperature indices. The AMO actually provides a greater impact on temperatures than the ENSO.

– The ENSO and the AMO impact temperatures directly and continuously on a monthly basis. Any smoothing of the data or even using annual temperature data just reduces the information which can be extracted.

– The ENSO’s impact on temperatures is lagged by 3 months while the AMO seems to be more immediate. This model uses the Nino 3.4 region anomaly since it seems to be the most indicative of the underlying El Nino and La Nina trends.

– When the ENSO and the AMO impacts are adjusted for, all that is left is the global warming signal and a white noise error.

– The ENSO and the AMO are capable of explaining almost all of the natural variation in the climate.

– We can finally answer the question of how much global warming has there been to date and how much has occurred since 1979 for example. And, yes, there has been global warming but the amount is much less than global warming models predict and the effect even seems to be slowing down since 1979.

– Unfortunately, there is not currently a good forecast model for the ENSO or AMO so this method will have to focus on current and past temperatures versus providing forecasts for the future.

And now to the good part, here is what the reconstruction looks like for the Hadley Centre’s HadCRUT3 global monthly temperature series going back to 1871 – 1,652 data points.

I will walk you through how this method was developed since it will help with understanding some of its components.

Let’s first look at the Nino 3.4 region anomaly going back to 1871 as developed by Trenberth (actually this index is smoothed but it is the least smoothed data available).

– The 1877-78 El Nino was the biggest event on record. The anomaly peaked at +3.4C in Nov, 1877 and by Feb, 1878, global temperatures had spiked to +0.364C or nearly 0.7C above the background temperature trend of the time.

– The 1997-98 El Nino produced similar results and still holds the record for the highest monthly temperature of +0.749C in Feb, 1998.

– There is a lag of about 3 months in the impact of ENSO on temperatures. Sometimes it is only 2 months, sometimes 4 months and this reconstruction uses the 3 month lag.

– Going back to 1871, there is no real trend in the Nino 3.4 anomaly which indicates it is a natural climate cycle and is not related to global warming in the sense that more El Ninos are occurring as a result of warming. This point becomes important because we need to separate the natural variation in the climate from the global warming influence.

The AMO anomaly has longer cycles than the ENSO.

– While the Nino 3.4 region can spike up to +3.4C, the AMO index rarely gets above +0.6C anomaly.

– The long cycles of the AMO matches the major climate shifts which have occurred over the last 130 years. The downswing in temperatures from 1890 to 1915, the upswing in temps from 1915 to 1945, the decline from 1946 to 1975 and the upswing in temps from 1975 to 2005.

– The AMO also has spikes during the major El Nino events of 1877-88 and 1997-98 and other spikes at different times.

– It is apparent that the major increase in temperatures during the 1997-98 El Nino was also caused by the AMO anomaly. I think this has lead some to believe the impact of ENSO is bigger than it really is and has caused people to focus too much on the ENSO.

– There is some autocorrelation between the ENSO and the AMO given these simultaneous spikes but the longer cycles of the AMO versus the short sharp swings in the ENSO means they are relatively independent.

– As well, the AMO appears to be a natural climate cycle unrelated to global warming.

When these two ocean indices are regressed against the monthly temperature record, we have a very good match.

– The coefficient for the Nino 3.4 region at 0.058 means it is capable of explaining changes in temps of as much as +/- 0.2C.

– The coefficient for the AMO index at 0.51 to 0.75 indicates it is capable of explaining changes in temps of as much as +/- 0.3C to +/- 0.4C.

– The F-statistic for this regression at 222.5 means it passes a 99.9% confidence interval.

But there is a divergence between the actual temperature record and the regression model based solely on the Nino and the AMO. This is the real global warming signal.

The global warming signal (which also includes an error, UHI, poor siting and adjustments in the temperature record as demonstrated by Anthony Watts) can be now be modeled against the rise in CO2 over the period.

– Warming occurs in a logarithmic relationship to CO2 and, consequently, any model of warming should be done on the natural log of CO2.

– CO2 in this case is just a proxy for all the GHGs but since it is the biggest one and nitrous oxide is rising at the same rate, it can be used as the basis for the warming model.

This regression produces a global warming signal which is about half of that predicted by the global warming models. The F statistic at 4,308 passes a 99.9% confidence interval.

– Using the HadCRUT3 temperature series, warming works out to only 1.85C per doubling of CO2.

– The GISS reconstruction also produces 1.85C per doubling while the NCDC temperature record only produces 1.6C per doubling.

– Global warming theorists are now explaining the lack of warming to date is due to the deep oceans absorbing some of the increase (not the surface since this is already included in the temperature data). This means the global warming model prediction line should be pushed out 35 years, or 75 years or even 100s of years.

Here is a depiction of how logarithmic warming works. I’ve included these log charts because it is fundamental to how to regress for CO2 and it is a view of global warming which I believe many have not seen before.

The formula for the global warming models has been constructed by myself (I’m not even sure the modelers have this perspective on the issue) but it is the only formula which goes through the temperature figures at the start of the record (285 ppm or 280 ppm) and the 3.25C increase in temperatures for a doubling of CO2. It is curious that the global warming models are also based on CO2 or GHGs being responsible for nearly all of the 33C greenhouse effect through its impact on water vapour as well.

The divergence, however, is going to be harder to explain in just a few years since the ENSO and AMO-adjusted warming observations are tracking farther and farther away from the global warming model’s track. As the RSS satellite log warming chart will show later, temperatures have in fact moved even farther away from the models since 1979.

The global warming models formula produces temperatures which would be +10C in geologic time periods when CO2 was 3,000 ppm, for example, while this model’s log warming would result in temperatures about +5C at 3,000 ppm. This is much closer to the estimated temperature history of the planet.

This method is not perfect. The overall reconstruction produces a resulting error which is higher than one would want. The error term is roughly +/-0.2C but the it does appear to be strictly white noise. It would be better if the resulting error was less than +/- 0.2C but it appears this is unavoidable in something as complicated as the climate and in the measurement errors which exist for temperature, the ENSO and the AMO.

This is the error for the reconstruction of GISS monthly data going back to 1880.

There does not appear to be a signal remaining in the errors for another natural climate variable to impact the reconstruction. In reviewing this model, I have also reviewed the impact of the major volcanoes. All of them appear to have been caught by the ENSO and AMO indices which I imagine are influenced by volcanoes. There appears to be some room to look at a solar influence but this would be quite small. Everyone is welcome to improve on this reconstruction method by examining other variables, other indices.

Overall, this reconstruction produces an r^2 of 0.783 which is pretty good for a monthly climate model based on just three simple variables. Here is the scatterplot of the HadCRUT3 reconstruction.

This method works for all the major monthly temperature series I have tried it on.

Here is the model for the RSS satellite-based temperature series.

Since 1979, warming appears to be slowing down (after it is adjusted for the ENSO and the AMO influence.)

The model produces warming for the RSS data of just 0.046C per decade which would also imply an increase in temperature of just 0.7C for a doubling of CO2 (and there is only 0.4C more to go to that doubling level.)

Looking at how far off this warming trend is from the models can be seen in this zoom-in of the log warming chart. If you apply the same method to GISS data since 1979, it is in the same circle as the satellite observations so the different agencies do not produce much different results.

There may be some explanations for this even wider divergence since 1979.

– The regression coefficient for the AMO increases from about 0.51 in the reconstructions starting in 1880 to about 0.75 when the reconstruction starts in 1979. This is not an expected result in regression modelling.

– Since the AMO was cycling upward since 1975, the increased coefficient might just be catching a ride with that increasing trend.

– I believe a regression is a regression and we should just accept this coefficient. The F statistic for this model is 267 which would pass a 99.9% confidence interval.

– On the other hand, the warming for RSS is really at the very lowest possible end for temperatures which might be expected from increased GHGs. I would not use a formula which is lower than this for example.

– The other explanation would be that the adjustments of old temperature records by GISS and the Hadley Centre and others have artificially increased the temperature trend prior to 1979 when the satellites became available to keep them honest. The post-1979 warming formulae (not just RSS but all of them) indicate old records might have been increased by 0.3C above where they really were.

– I think these explanations are both partially correct.

This temperature reconstruction method works for all of the major temperature series over any time period chosen and for the smaller zonal components as well. There is a really nice fit to the RSS Tropics zone, for example, where the Nino coefficient increases to 0.21 as would be expected.

Unfortunately, the method does not work for smaller regional temperature series such as the US lower 48 and the Arctic where there is too much variation to produce a reasonable result.

I have included my spreadsheets which have been set up so that anyone can use them. All of the data for HadCRUT3, GISS, UAH, RSS and NCDC is included if you want to try out other series. All of the base data on a monthly basis including CO2 back to 1850, the AMO back to 1856 and the Nino 3.4 region going back to 1871 is included in the spreadsheet.

The model for monthly temperatures is “here” and for annual temperatures is “here” (note the annual reconstruction is a little less accurate than the monthly reconstruction but still works).

I have set-up a photobucket site where anyone can review these charts and others that I have constructed.

So, we can now adjust temperatures for the natural variation in the climate caused by the ENSO and the AMO and this has provided a better insight into global warming. The method is not perfect, however, as the remaining error term is higher than one would want to see but it might be unavoidable in something as complicated as the climate.

I encourage everyone to try to improve on this method and/or find any errors. I expect this will have to be taken into account from now on in global warming research. It is a simple regression.

SUPPLEMENTAL INFO NOTE: Bill has made the Excel spreadsheets with data and graphs used for this essay available to me, and for those interested in replication and further investigation, I’m making them available here on my office webserver as a single ZIP file

Over the past 2,000 years, the average temperature of the northern hemisphere has exceeded natural variability (+/-2 std dev) 3 times: The Medieval Warm Period, the Little Ice Age and the modern warming. Humans didn’t cause at least two of the three and the current one only exceeds natural variability by about 0.2 C. And this is a maximum, because the instrumental data have much higher resolution than the proxy data.

Sometimes I feel like a strange attractor for weather station chaos. Here I am at home tonight minding my own business, in my home office and I have the TV on. JEOPARDY comes on. Alex Trebek announces the categories…and I pay little attention until the last one is announced and he says “National Weather Service”. I practically got whiplash turning to look at the TV. In 25 years of watching this TV program, that is a category I never expected to see.

Then to explain the category, up pops one of the “clue crew” people standing in front of the NWS office in Upton, New York, in the parking lot.

I didn’t hear a single word she said, because my eyes were transfixed on what was right behind her: a Stevenson Screen and MMTS just a couple of feet from the parking lot with the brick walls of the NWS office right behind it.

WTH!? Then it was gone.

I waited out the first round of JEOPARDY hoping to see more, but the contestants avoided the NWS category. Finally with nothing left they started into it. Then there it was again, the NWS station with visitor parking privileges.

After acing the category (the final answer was supercell) I decided to see if I could find this NWS office in Upton and maybe get a picture. I found that and more.

My first simple Google Image search found it right away, a photo taken during an open house on a Skywarn page:

It did show the proximity to the brick building, but it really didn’t tell the whole story of what I saw in the TV shot. What was funny was that in the JEOPARDY segment, the NWS employees had apparently done some “sprucing up” and had painted the legs of the shelter and the MMTS mount pole a blue color to match the logo color of the NWS emblem over the office door:

I found the above picture and the one below at the NWS Upton web page where they have a “virtual tour” of the facility. Here is another angle from the web page that shows the overall NWS complex, including the NEXRAD Doppler radar tower:

Looking at the style of the automobiles, I’m guessing these photos were taken sometime in the early 90’s when this office was opened. What is interesting about these photos, besides the siting issues with proximity to parking and the building, is the fact that the Stevenson Screen door is facing SOUTH rather than the requisite north. The idea is to keep direct sunlight from hitting the thermometers when readings are taken.

I thought perhaps this station is purely a “figurehead” used for school tours, etc, but then I thought: “Why would they want to show it being done incorrectly?”. I checked the NCDC MMS meta-database to see if the station was active. Oddly I couldn’t find the right station in Upton in the database. Poking around again at the NWS Uptoon website I found out why: This used to be New York City’s station. It was once on top of the RCA building as I discovered from their virtual tour:

Dec. 28, 1960 to Oct. 24, 1993

RCA/GE Building
30 Rockefeller Center NY, NY
Mezzanine Level

Once knowing it was the NYC station and not “Upton”, I was able to find it in the NCDC MMS metadatabase and determine that indeed it is an active station. Fortunately it is listed as NOT being part of the climate network, and neither USHCN or GISS uses this station.

From the lat/lon posted there (40.86667 -72.86667 ) I was able to locate the station on Google Earth:

It turns out that the NWS Forecasts Office happens to be on the grounds of the Brookhaven National Laboratory, and the address is at 175 Brookhaven Ave, Upton, NY.

It seems that there is ample room in the grassy area in the rear to place a weather station, rather than putting it up front in the parking lot. A Microsoft Live maps image also shows the proximity issues up front and with the building.

Of course looking at this photo, it would now seem that the rear of the building might not be the best choice either with that bank of 5 a/c units back there. But it could find a site further away to the rear or perhaps cleared more trees.

Even if this station isn’t in the climate network, it really does beg the question: why does the NWS blatantly flaunt their own 100 foot rule? Further, since this NWS Office is located on the grounds of the Brookhaven National Laboratory, wouldn’t you think they’d want to put their absolute best sceintific foot forward?

Even is this station is only used to show school kids what a weather station looks like and how it is operated, why not do it right and show proper placement away from biases, proper door alignment on the screen, and explain why these things are important for proper measurements?

“It’s About Jobs”

Remarks by the Vice President Introducing President Trump’s Statement on the Paris Accord

The Rose Garden

3:29 P.M. EDT

THE VICE PRESIDENT: Good afternoon. Secretary Mnuchin, Secretary Ross, EPA Administrator Scott Pruitt, members of Congress, distinguished guests, on behalf of the First Family, welcome to the White House. (Applause.)

It’s the greatest privilege of my life to serve as Vice President to a President who is fighting every day to make America great again.

Since the first day of this administration, President Donald Trump has been working tirelessly to keep the promises that he made to the American people. President Trump has been reforming healthcare, enforcing our laws, ending illegal immigration, rebuilding our military. And this President has been rolling back excessive regulations and unfair trade practices that were stifling American jobs.

Thanks to President Trump’s leadership, American businesses are growing again; investing in America again; and they’re creating jobs in this country instead of shipping jobs overseas. Thanks to President Donald Trump, America is back. (Applause.)

And just last week we all witnessed the bold leadership of an American President on the world stage, putting America first. From the Middle East, to Europe, as leader of the free world, President Trump reaffirmed historic alliances, forged new relationships, and called on the wider world to confront the threat of terrorism in new and renewed ways.

And by the action, the President will announce today, the American people and the wider world will see once again our President is choosing to put American jobs and American consumers first. Our President is choosing to put American energy and American industry first. And by his action today, President Donald Trump is choosing to put the forgotten men and women of America first.

So with gratitude for his leadership — (applause) — and admiration for his unwavering commitment to the American people, it is now my high honor and distinct privilege to introduce to all of you, the President of the United States of America, President Donald Trump. (Applause.)

END
3:31 P.M. EDT

President Donald Trump is the President of these United States of America. He took the following oath of office:

“I do solemnly swear that I will faithfully execute the Office of President of the United States, and will to the best of my ability, preserve, protect and defend the Constitution of the United States.”

Since the Paris climate agreement was effectively a treaty and the prior occupant of the Office of President of the United States failed to submit it to the Senate for ratification, the only way President Trump could uphold his oath of office was to either withdraw from the agreement or submit it to the Senate where is would not be ratified.

That said, President Trump campaigned on the promise to put America, including American industry and energy, first.